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Greatest papers with code
RLCard: A Toolkit for Reinforcement Learning in Card Games
• datamllab/rlcard
• 
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
BOARD GAMES GAME OF POKER MULTI-AGENT REINFORCEMENT LEARNING
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
Deep Learning for General Game Playing with Ludii and Polygames
• facebookincubator/Polygames
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Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games.
Move Evaluation in Go Using Deep Convolutional Neural Networks
• jmgilmer/GoCNN
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The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo.
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.
The Text-Based Adventure AI Competition
In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games.
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game.
Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration
ExIt involves training a policy to mimic the search behaviour of a tree search algorithm - such as Monte-Carlo tree search - and using the trained policy to guide it.
Biasing MCTS with Features for General Games
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games.